Periodic Reporting for period 1 - OptiQ (Non-standard data and image processing - from nonlinear Optics to Quantum computing)
Reporting period: 2023-01-01 to 2024-12-31
The OptiQ project explores how quantum technologies can improve data processing, image analysis, and secure communication. While quantum computing holds great promise, current systems are still limited by hardware imperfections and computational noise. It addresses these challenges by developing quantum-based methods for image processing and security, leveraging advances in quantum optics and AI. Today’s quantum computers, known as Noisy Intermediate-Scale Quantum (NISQ) devices, struggle with errors and scalability, limiting their practical applications. However, they already show potential for optimizing data processing, enhancing encryption, and accelerating machine learning. OptiQ focuses on unlocking this potential by improving quantum-enhanced image processing, exploring quantum security protocols, and developing augmented reality tools for designing quantum experiments. To bridge the gap between quantum theory and real-world applications, OptiQ will develop a new way to encode images in quantum systems, enabling more efficient data processing. It will also advance hybrid quantum-classical algorithms for image recognition and object detection. In secure communication, the project will investigate how entangled photons can enhance encryption and ensure safe data transmission, while also exploring the role of human interaction in quantum experiments. A key innovation is an AR tool for designing and visualizing quantum optical circuits, making complex quantum experiments more accessible. By integrating quantum computing, optics, and AI, OptiQ aims to make quantum-enhanced technologies more practical and secure.
During the 2023-2024 period, the OptiQ project made significant progress in quantum computing, image processing, and secure communication. In quantum communication and computing, an experimental framework for entangled photon generation was developed using ppKTP and BBO crystals. An optical setup for photon entanglement, including a narrow-bandwidth laser and coincidence detection system, was implemented. Work focused on refining photon detection and improving experimental conditions. Initial tests on quantum teleportation and image representation in optical circuits were conducted. The project also began a Bell’s inequality validation experiment with human participation, developing an interactive quantum game and automating optical setups to enhance precision.
In augmented reality for quantum optical systems, a holographic AR-based quantum optics simulator was developed, enabling interactive design and visualization of quantum circuits. Fiducial marker recognition and 3D spatial mapping were implemented for precise optical setup alignment. A prototype AR system for quantum optics design was created, allowing researchers to construct and visualize experiments in real time. An optical device recognition system was also developed and integrated into laboratory setups, ensuring accurate AR-based visualization of quantum experiments. Ongoing work includes improving real-time visualization of quantum states and interaction features in AR.
For quantum image processing and optimization on NISQ computers, the General Quantum Image Encoding (GQIE) framework was developed, unifying different quantum image encoding methods for systematic testing across multiple quantum platforms. More than ten encoding methods were implemented and tested on IBM, AWS, and Xanadu quantum hardware. Extensive error analysis and mitigation experiments led to the development of the Phase Distortion Unraveling (PDU) technique, which enhances image reconstruction accuracy by correcting quantum noise effects. A hybrid quantum-classical object detection algorithm was developed and tested on real quantum hardware, showing promising results. A novel classifier representation using tensor fields was introduced, leveraging geometric structures for improved flexibility in quantum machine learning.
In quantum security research, a comprehensive assessment of error sources in NISQ quantum systems was conducted, analyzing decoherence, gate noise, and measurement errors. AI-assisted quantum error mitigation was explored, and generative adversarial networks (GANs) were implemented to enhance quantum image reconstruction, significantly reducing errors. A differential enhancement measurement simulator was developed to improve photon correlation detection, with successful initial tests demonstrating feasibility for low-voltage and low-current quantum security applications. Research on quantum-resistant security protocols was initiated, focusing on detecting quantum-enabled attacks and vulnerabilities in hybrid quantum-classical security systems.
Across all research areas, OptiQ has successfully developed experimental frameworks, validated key quantum algorithms, and created tools to support quantum-enhanced computing, security, and optical design. The project remains on track to achieve its final objectives by 2026, with ongoing research and prototype testing advancing toward practical implementations. Future work will expand experimental validations, refine hybrid quantum-classical approaches, and integrate quantum security solutions with real-world communication systems. The continued development of quantum-enhanced image processing, AR-assisted quantum design, and secure quantum communication techniques will contribute to advancements in artificial intelligence, cybersecurity, and quantum information science.
In augmented reality for quantum optical systems, a holographic AR-based quantum optics simulator was developed, enabling interactive design and visualization of quantum circuits. Fiducial marker recognition and 3D spatial mapping were implemented for precise optical setup alignment. A prototype AR system for quantum optics design was created, allowing researchers to construct and visualize experiments in real time. An optical device recognition system was also developed and integrated into laboratory setups, ensuring accurate AR-based visualization of quantum experiments. Ongoing work includes improving real-time visualization of quantum states and interaction features in AR.
For quantum image processing and optimization on NISQ computers, the General Quantum Image Encoding (GQIE) framework was developed, unifying different quantum image encoding methods for systematic testing across multiple quantum platforms. More than ten encoding methods were implemented and tested on IBM, AWS, and Xanadu quantum hardware. Extensive error analysis and mitigation experiments led to the development of the Phase Distortion Unraveling (PDU) technique, which enhances image reconstruction accuracy by correcting quantum noise effects. A hybrid quantum-classical object detection algorithm was developed and tested on real quantum hardware, showing promising results. A novel classifier representation using tensor fields was introduced, leveraging geometric structures for improved flexibility in quantum machine learning.
In quantum security research, a comprehensive assessment of error sources in NISQ quantum systems was conducted, analyzing decoherence, gate noise, and measurement errors. AI-assisted quantum error mitigation was explored, and generative adversarial networks (GANs) were implemented to enhance quantum image reconstruction, significantly reducing errors. A differential enhancement measurement simulator was developed to improve photon correlation detection, with successful initial tests demonstrating feasibility for low-voltage and low-current quantum security applications. Research on quantum-resistant security protocols was initiated, focusing on detecting quantum-enabled attacks and vulnerabilities in hybrid quantum-classical security systems.
Across all research areas, OptiQ has successfully developed experimental frameworks, validated key quantum algorithms, and created tools to support quantum-enhanced computing, security, and optical design. The project remains on track to achieve its final objectives by 2026, with ongoing research and prototype testing advancing toward practical implementations. Future work will expand experimental validations, refine hybrid quantum-classical approaches, and integrate quantum security solutions with real-world communication systems. The continued development of quantum-enhanced image processing, AR-assisted quantum design, and secure quantum communication techniques will contribute to advancements in artificial intelligence, cybersecurity, and quantum information science.
During 2023-2024, the OptiQ project advanced quantum computing, image processing, secure communication, and augmented reality for quantum optics, making quantum technologies more practical and applicable.
A key achievement is the General Quantum Image Encoding framework, enabling benchmarking of quantum image encoding methods on IBM, AWS, and Xanadu platforms. Over ten models were tested, leading to Phase Distortion Unraveling, an error mitigation technique improving quantum image reconstruction. These advancements enhance quantum machine learning and data processing, though further optimization is needed for practical use.
In quantum communication and security, an experimental setup for entangled photon generation was developed, supporting quantum key distribution and Bell’s inequality validation. A differential enhancement measurement simulator improved photon correlation detection without high-frequency sampling. Further work will focus on integrating these advances into quantum networks.
The project also developed a holographic augmented reality-based quantum optics simulator, allowing real-time visualization and precise alignment of optical setups. This tool simplifies quantum experiments and will be expanded for broader research and training applications.
In quantum security, AI-assisted error mitigation, including generative adversarial networks, improved quantum image reconstruction. Research on quantum-resistant security protocols identified vulnerabilities in hybrid quantum-classical systems, laying the groundwork for quantum attack detection.
Future work will refine quantum image processing, expand security testing, and enhance augmented reality tools, helping transition quantum computing toward real-world applications. Industry collaboration and regulatory alignment will be crucial for commercialization and adoption.
A key achievement is the General Quantum Image Encoding framework, enabling benchmarking of quantum image encoding methods on IBM, AWS, and Xanadu platforms. Over ten models were tested, leading to Phase Distortion Unraveling, an error mitigation technique improving quantum image reconstruction. These advancements enhance quantum machine learning and data processing, though further optimization is needed for practical use.
In quantum communication and security, an experimental setup for entangled photon generation was developed, supporting quantum key distribution and Bell’s inequality validation. A differential enhancement measurement simulator improved photon correlation detection without high-frequency sampling. Further work will focus on integrating these advances into quantum networks.
The project also developed a holographic augmented reality-based quantum optics simulator, allowing real-time visualization and precise alignment of optical setups. This tool simplifies quantum experiments and will be expanded for broader research and training applications.
In quantum security, AI-assisted error mitigation, including generative adversarial networks, improved quantum image reconstruction. Research on quantum-resistant security protocols identified vulnerabilities in hybrid quantum-classical systems, laying the groundwork for quantum attack detection.
Future work will refine quantum image processing, expand security testing, and enhance augmented reality tools, helping transition quantum computing toward real-world applications. Industry collaboration and regulatory alignment will be crucial for commercialization and adoption.